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Reliability Analysis Usefulness

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Reliability analysis refers to the fact that a scale should consistently reflect the construct it is measuring. There are certain times and situations where it can be useful. Statistics Solutions is the country 's leader in statistical data analysis and can assist with reliability analysis for your dissertation, thesis or research project. Contact Statistics Solutions today for a free 30-minute consultation. An aspect in which the researcher can use reliability analysis is when two observations under study that are equivalent to each other in terms of the construct being measured also have the equivalent outcome. There is a popular technique called the split half reliability. This method splits the data into two parts. The score for each participant …show more content…

In this type of reliability analysis, the previous fact should remain true for all the participants. The major problem is that there are several ways in which a set of data can be divided into two parts, and therefore the outcome could be numerous. There are basically two versions of alpha in reliability analysis. The first version is the normal version. The second version is the standardized version. The normal version of alpha is applicable when the items on a scale are summed to produce a single score for that scale. The standardized version of alpha is applicable when the items on a scale are standardized before they are summed up. To attain reliability in the data, the coding done by the researcher should be …show more content…

Strong weak relationship was measured using the distance (range) 0 to 1. Correlations have the possibility of testing the hypothesis of two directions (two-tailed). If the direct correlation was found a positive correlation; otherwise if the correlation coefficient is negative, correlation is not unidirectional. What is meant by the correlation coefficient is a statistical measurement covariant or association between two variables. If the correlation coefficient is found not equal to zero (0), then there is a relationship between two variables. If the correlation found +1. then the relationship is called a perfect correlation or perfect linear relationship with a slope (slope) positive. Vice versa. if the correlation coefficient is found -1. then the relationship is called a perfect correlation or perfect linear relationship with a slope (slope) is negative. In a perfect correlation unnecessary testing hypotheses about the significance among the variables that are correlated, because the two variables have a perfect linear relationship. This means that the variable X has a very strong relationship with variable Y. If the correlation is zero (0), then there is no relationship between these two

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